arXiv:2512.08960v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables efficient Continual Learning but often suffers from catastrophic forgetting due to destructive interference between tasks. Our analysis reveals that this degradation is primarily driven by antagonistic directional updates where new task…
arXiv:2512.08964v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) achieve strong performance on graph-structured data, but are notoriously vulnerable to small, carefully crafted perturbations of the graph structure. Most existing structure-based attacks rely on gradient-based heuristics or local connectivity patterns,…
arXiv:2512.08957v1 Announce Type: new Abstract: User behavior prediction at scale remains a critical challenge for online B2C platforms. Traditional approaches rely heavily on task-specific models and domain-specific feature engineering. This is time-consuming, computationally expensive, and requires domain expertise and therefore…
arXiv:2512.08959v1 Announce Type: new Abstract: We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks across 14 publicly available EEG datasets, including epilepsy, schizophrenia, Parkinson’s disease, OCD, and…
arXiv:2512.08956v1 Announce Type: new Abstract: K-Nearest Neighbors (KNN) is one of the most used ML classifiers. However, if we observe closely, standard distance-weighted KNN and relative variants assume all ‘k’ neighbors are equally reliable. In heterogeneous feature space, this becomes…
arXiv:2512.08955v1 Announce Type: new Abstract: Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) networks, offering massive spatial degrees of freedom. Despite these advantages, the coexistence of near-field and far-field effects in hybrid-field channels presents significant…
arXiv:2512.08954v1 Announce Type: new Abstract: In the process of patient diagnosis, non-invasive measurements are widely used due to their low risks and quick results. Electrocardiogram (ECG), as a non-invasive method to collect heart activities, is used to diagnose cardiac conditions.…
arXiv:2407.04958v2 Announce Type: replace Abstract: Normalizing Flows (NFs) are widely used in deep generative models for their exact likelihood estimation and efficient sampling. However, they require substantial memory since the latent space matches the input dimension. Multi-scale architectures address this…
arXiv:2512.08965v1 Announce Type: new Abstract: Language Models (LMs) struggle with complex, interdependent instructions, particularly in high-stakes domains like finance where precision is critical. We introduce FIFE, a novel, high-difficulty benchmark designed to assess LM instruction-following capabilities for financial analysis tasks.…
arXiv:2505.15138v2 Announce Type: replace Abstract: This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) with general parametrization. We propose a Primal-Dual Natural Actor-Critic algorithm that adeptly manages constraints while ensuring a high convergence rate. In particular, our algorithm…
